Crowley Daniel, Becker Daniel, Washburne Alex, Plowright Raina
Department of Microbiology and Immunology, Montana State University, Bozeman, MT 59717, USA.
Department of Biology, Indiana University, Bloomington, IN 47405, USA.
Vaccines (Basel). 2020 May 17;8(2):228. doi: 10.3390/vaccines8020228.
Bats host a number of pathogens that cause severe disease and onward transmission in humans and domestic animals. Some of these pathogens, including henipaviruses and filoviruses, are considered a concern for future pandemics. There has been substantial effort to identify these viruses in bats. However, the reservoir hosts for Ebola virus are still unknown and henipaviruses are largely uncharacterized across their distribution. Identifying reservoir species is critical in understanding the viral ecology within these hosts and the conditions that lead to spillover. We collated surveillance data to identify taxonomic patterns in prevalence and seroprevalence and to assess sampling efforts across species. We systematically collected data on filovirus and henipavirus detections and used a machine-learning algorithm, phylofactorization, in order to search the bat phylogeny for cladistic patterns in filovirus and henipavirus infection, accounting for sampling efforts. Across sampled bat species, evidence for filovirus infection was widely dispersed across the sampled phylogeny. We found major gaps in filovirus sampling in bats, especially in Western Hemisphere species. Evidence for henipavirus infection was clustered within the Pteropodidae; however, no other clades have been as intensely sampled. The major predictor of filovirus and henipavirus exposure or infection was sampling effort. Based on these results, we recommend expanding surveillance for these pathogens across the bat phylogenetic tree.
蝙蝠携带多种病原体,这些病原体可在人类和家畜中引发严重疾病并进一步传播。其中一些病原体,包括亨尼帕病毒和丝状病毒,被认为是未来大流行的隐患。人们已付出大量努力在蝙蝠中识别这些病毒。然而,埃博拉病毒的自然宿主仍不明确,而且亨尼帕病毒在其分布范围内大多未得到充分研究。识别自然宿主物种对于了解这些宿主内的病毒生态学以及导致病毒外溢的条件至关重要。我们整理了监测数据,以确定患病率和血清阳性率的分类模式,并评估跨物种的采样工作。我们系统地收集了有关丝状病毒和亨尼帕病毒检测的数据,并使用一种机器学习算法——系统发育因子分解法,以便在蝙蝠系统发育树中搜索丝状病毒和亨尼帕病毒感染的分支模式,同时考虑到采样工作。在所有采样的蝙蝠物种中,丝状病毒感染的证据广泛分布在采样的系统发育树上。我们发现蝙蝠丝状病毒采样存在重大缺口,尤其是在西半球物种中。亨尼帕病毒感染的证据集中在狐蝠科内;然而,没有其他分支得到如此密集的采样。丝状病毒和亨尼帕病毒暴露或感染的主要预测因素是采样工作。基于这些结果,我们建议在整个蝙蝠系统发育树上扩大对这些病原体的监测。